Keeping track of different types of data entries and interdependencies among the different entries is a task for which computers are ideally suited, and modern society depends heavily on this capability. From social networking platforms to financial analysis applications, computers, along with robust communication networks, are able to propagate a change in one data item (e.g., a change in a cell of a spreadsheet or a change in a user's status on a social network) to other data items (e.g., a recalculation of a formula in a spreadsheet or an update of an emoticon on the devices of the user's friends).
One problem that arises with propagating changes among many interdependent data entries is that it can be very slow when the number of entries and interdependencies is high and when the entries are stored across different documents, databases, servers and different geographical locations of the servers. For example, those who work with large spreadsheets are familiar with the experience in which, when a change is made to one cell of a spreadsheet, the spreadsheet program spends a long time updating itself repeatedly as the formulas depending on the changed cell get recalculated, the formulas depending on those formulas get recalculated, and so on. Dependencies that cross documents or servers create similar delays.